CN117094594B - Intelligent detection, analysis and evaluation system for water seepage and water leakage of building engineering wall - Google Patents
Intelligent detection, analysis and evaluation system for water seepage and water leakage of building engineering wall Download PDFInfo
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Abstract
The invention belongs to the field of analysis and evaluation of engineering walls, and particularly relates to an intelligent detection, analysis and evaluation system for water seepage and water leakage of a building engineering wall. According to the invention, the wall body is detected by dividing each building detection area according to the key parts of the building, leakage data comprising the water content of the wall body of the building, the length of water marks on the wall body, the number of water drops, the mildew area of the wall body and the color change area of the wall body are obtained, so that the leakage severity evaluation coefficient is obtained, the potential water seepage possibility of each building key area is effectively detected, the leakage possibility of each residual area is evaluated and predicted, the expected work of water seepage and water leakage of the wall body of the building engineering is determined, the loss of wall body repair is reduced, and the management efficiency and timeliness are realized for the building.
Description
Technical Field
The invention belongs to the field of analysis and evaluation of engineering walls, and particularly relates to an intelligent detection, analysis and evaluation system for water seepage and water leakage of a building engineering wall.
Background
Water seepage and water leakage in constructional engineering are common engineering problems, and particularly in buildings exposed to humid environments in rainy seasons or for a long time, the appearance and the use function of the buildings can be affected, concrete structures can be damaged, and even personnel and property safety is endangered. With the continuous development of the building industry, construction managers have found many methods for monitoring water seepage and water leakage. However, the water seepage and leakage problems of the building engineering wall body after engineering delivery still exist, so that normal work and life of people are seriously influenced, the problems are found in time and repaired, long-term use and stability of a building are facilitated, and therefore periodic monitoring and evaluation of water seepage and leakage of the building engineering wall body are very necessary.
The existing engineering wall analysis and evaluation system is used for carrying out system monitoring and analysis on the building engineering wall, can meet the current requirements to a certain extent, still has certain defects and limitations, and is specifically embodied in: (1) The existing building wall body is relatively one-sided in data acquisition, and can not deeply acquire leakage data of a key area of a building, so that data analysis is incomplete, and the leakage severity of the key area of the building can not be accurately analyzed, thereby improving the cost for later-stage wall body leakage repair and increasing the risk of wall body leakage.
(2) The existing wall body water seepage and leakage analysis and evaluation system cannot match seepage sources according to existing data, can not realize effective control and evaluation on history left problems of seepage areas, cannot fundamentally distinguish existing seepage areas from potential seepage areas, and increases task quantity for subsequent seepage area detection.
(3) The existing wall body water seepage and water leakage analysis and evaluation system is not comprehensive enough in analysis of the potential water seepage area of the building, lacks data and theoretical support for seepage evaluation of the potential water seepage area, reduces the safety and stability of the wall body of the building, and is not beneficial to normal use of the building.
Disclosure of Invention
In view of this, in order to solve the problems set forth in the background art, a system for intelligently detecting, analyzing and evaluating water seepage and water leakage of a wall body of a building engineering is provided, and an intelligent technology and a data analysis method are utilized to monitor and analyze the water seepage and water leakage process of the wall body of the building engineering.
The aim of the invention can be achieved by the following technical scheme: the system comprises a building dividing module, a building collecting module, a building comprehensive analysis module, a leakage grade evaluation and identification module, a leakage source matching module, a management database and a leakage early warning processing module.
The building dividing module is used for dividing each building detection area according to the key parts of the building to be detected and numbering each building detection area sequentially.
The building acquisition module is used for acquiring leakage conditions of all building detection areas, so as to obtain leakage data of all building detection areas.
The building comprehensive analysis module is used for processing and analyzing according to the leakage data of each building detection area, so as to obtain the leakage severity evaluation coefficient of each building detection area.
The leakage grade evaluation and identification module is used for analyzing the leakage severity evaluation coefficients of the detection areas of the buildings, and further screening the water seepage areas and the residual areas of the buildings to be detected.
The leakage source matching module is used for analyzing leakage sources of all the water seepage areas according to leakage associated data corresponding to all the historical water seepage of the building to be detected in a set historical period and displaying the leakage sources of all the water seepage areas.
The management database is used for storing leakage associated data corresponding to each time of historical water seepage of the building to be detected in a set historical period and standard leakage data meeting the building engineering standard, and storing leakage severity evaluation coefficients corresponding to each time of detection of each building detection area of the building to be detected in a set historical period and leakage overlapping area difference allowed by the building under the safety condition.
The leakage early warning processing module is used for detecting leakage conditions of all the residual areas of the building to be detected, further evaluating leakage probability coefficients of all the residual areas, and carrying out corresponding early warning processing.
Further, the leakage data comprise the water content of the building wall, the length of water marks on the wall, the number of water drops, the mildew area of the wall and the color change area of the wall.
Further, the collecting the leakage condition of each building detection area specifically includes: the building to be detected is divided into building detection areas according to the key parts, and the building detection areas are sequentially numbered as i=1, 2.
And setting humidity sensors in key areas of the detection areas of the buildings, identifying characters on the humidity sensors through image processing and pattern identification technologies, extracting the identified characters, and transmitting the extracted characters to a management database to obtain the water content Q i of the building wall body in the key areas of the detection areas of the buildings.
Setting a regular snapshot camera in a key area of each building detection area, carrying out infrared thermal imaging technology on the key area of each building detection area according to a set time node, photographing and detecting to obtain an infrared thermal imaging image for visualization, selecting an area with a bright color value in the infrared thermal imaging image to form a photo image according to the sensitivity of the infrared thermal imaging device and the background temperature of the picture, carrying out identification processing on the detected wall water mark in the image, and measuring to obtain the wall water mark length L i of the key area of each building detection area.
And (3) carrying out gray value processing on the photo image corresponding to the key area of each building detection area, extracting gray values of pixel points obtained after gray processing of each building detection area, comparing the gray values of the pixel points with a set gray value range of water drops or mould, judging the pixel points as water drops if the gray value of the pixel points of a certain building detection area is within the set gray value range of the water drops, counting the number of building water drops in the key area of each building detection area and recording as N i, judging the pixel points as mould if the gray value of the pixel points of the certain building detection area is within the set gray value range of the mould, and counting the mould area of the building wall of the key area of each building detection area and recording as S i.
And (3) carrying out binarization processing on the photo image corresponding to the key area of each building detection area, setting the gray value of the pixel point on the image to be 0 or 255, enabling the whole image to show obvious visual effects of only black and white, counting the image area of the key area of each building detection area expressed as 0, obtaining the building wall color change area of the key area of each building detection area, and recording as T i.
Putting the water content Q i, the length L i of the water mark, the number N i of water drops, the mildew area S i and the color change area T i of the wall body of the building in the key area of each building detection area into a formulaAnd obtaining a leakage severity assessment coefficient theta i of each building detection area, wherein Q ', L ', N ', S ', T ' are respectively set building wall water content, wall water mark length, wall water drop number, wall mildew area and wall color change area which meet the building engineering standard, and mu 1、μ2、μ3、μ4、μ5 is respectively set influence factors corresponding to the building wall water content, the wall water mark length, the water drop number, the wall color change area and the wall mildew area.
Further, the analysis of the leakage severity assessment coefficient of each building detection area specifically includes: comparing the leakage severity assessment coefficient of each building detection area with a set leakage severity assessment coefficient threshold, if the leakage severity assessment coefficient of a certain building detection area is larger than the set leakage severity assessment coefficient threshold, taking the building detection area as a water seepage area, otherwise, taking the building detection area as a residual area, and further screening each water seepage area and each residual area of the building to be detected.
Further, the analyzing the leakage source of each water seepage area specifically comprises: extracting leakage related data corresponding to each historical water seepage of a building to be detected in a set historical period in a management database, wherein the leakage related data comprise leakage severity assessment coefficients, leakage areas and water seepage sources, and the leakage severity assessment coefficients of the water seepage areas screened out according to the leakage severity assessment coefficients of the detection areas of the building are marked as theta t, t is the number t=1, 2 and..s of the water seepage areas.
And (3) obtaining leakage areas of the water seepage areas, comparing the leakage areas with leakage areas of each historical water seepage of the building to be detected in a set historical period, and recording the leakage overlapping area of each water seepage area and the leakage area corresponding to each historical water seepage in the set historical period as S ty, wherein y is the number of the leakage area corresponding to each historical water seepage, and y=1, 2.
Substituting the leakage severity evaluation coefficient of each water seepage area and the leakage overlapping area of each water seepage area corresponding to each historical water seepage area in the set historical period into the information coincidence degree formulaObtaining the information coincidence degree xi ty,θ'y and delta S 'of each seepage area and each seepage area corresponding to each historical seepage in a set historical period, wherein the information coincidence degree xi ty,θ'y and delta S' are the seepage severity assessment coefficient corresponding to the y-th historical seepage in the set historical period of the building to be detected, the allowable seepage overlapping area difference value of the building under the safety condition, alpha 1、α2 is the set seepage severity assessment coefficient of the seepage area and the weight influence factor corresponding to the seepage area, P 1、P2 is a set constant, and e is a natural constant
Further, the analyzing the leakage source of each water seepage area specifically further comprises: and taking each historical water seepage of which the corresponding information coincidence degree of each water seepage area is larger than a set information coincidence degree threshold value as each reference water seepage, further extracting water seepage sources corresponding to each historical water seepage of a building to be detected in a management database in a set historical period, obtaining water seepage sources corresponding to each reference water seepage of each water seepage area, further obtaining the quantity of each water seepage source corresponding to each water seepage area, screening the water seepage sources with the largest quantity corresponding to each water seepage area, and taking the water seepage sources as the water seepage sources of each water seepage area.
Further, the detecting the leakage condition of each residual area of the building specifically includes: extracting a leakage severity assessment coefficient theta mv corresponding to each detection of each residual area of a building to be detected in a set historical time period in a management database, wherein m is the number of each residual area, m=1, 2,..l, v is the number of each detection corresponding to the residual area, v=1, 2,..u, and substituting the leakage severity assessment coefficient growth rate analysis formulaObtaining the leakage severity assessment coefficient growth rate RX m of each residual area, wherein theta m(v+1) is the leakage severity assessment coefficient of the mth residual area corresponding to the (v+1) th detection in the set historical time period of the building to be detected.
Extracting the environment temperature of each residual area of the building to be detected in the set historical time period from the weather bureau, recording as omega mv, and substituting the environment temperature evaluation coefficient change rate analysis formulaObtaining the change rate RH m of the environmental temperature evaluation coefficient of each residual area, wherein omega m(v+1) is the environmental temperature of the mth residual area corresponding to the (v+1) th detection in the set historical time period of the building to be detected.
Further, the estimating the leakage probability coefficient of each remaining area specifically includes: substituting the leakage severity evaluation coefficient growth rate and the environment temperature evaluation coefficient change rate of each residual area into the leakage probability coefficient lambda m＝ρ1*RXm+ρ2*RHm of each residual area to obtain the leakage probability coefficient lambda m of each residual area, wherein rho 1、ρ2 is the set leakage severity evaluation coefficient and the influence weight factor corresponding to the environment temperature, rho 1+ρ2 =1, comparing the obtained leakage probability coefficient of each residual area with the set leakage probability coefficient threshold, and repairing and maintaining the residual area if the leakage probability coefficient of a certain residual area is larger than or equal to the set leakage probability coefficient threshold.
The beneficial effects of the invention are as follows: (1) According to the invention, the building to be detected is divided into the building detection areas according to the key parts, and the data acquisition and comprehensive analysis are carried out on the building detection areas, so that the deep excavation of the wall body leakage key areas is realized, the water seepage and water leakage detection on the wall body key parts is effectively carried out in time, and more accurate data and analysis results are provided for the subsequent leakage grade evaluation.
(2) The invention has wide coverage for collecting the leakage data of the building wall, collects the water content of the wall, the length of water marks on the wall, the number of water drops, the mildew area of the wall and the color change area of the wall, analyzes the concrete embodiment of the leakage data to convert the leakage severity level, improves the comprehensiveness of leakage problems, reduces the cost of repairing the wall, and further avoids the risk of leakage of the wall.
(3) According to the invention, the leakage sources of the seepage areas of the building are matched, the history carry-over problem of the seepage areas is accurately prevented, the task amount of source detection of the seepage areas is reduced, the detection efficiency of the seepage areas of the wall body of the building is increased, the sources and the positions of the seepage areas are effectively mastered, and the reliability guarantee is provided for the subsequent wall body maintenance work.
(4) According to the invention, the system analysis is carried out on each residual area of the building wall, and effective data and theoretical support are provided for leakage assessment and prediction of each residual area according to the analysis of historical factors and environmental changes, so that the safety and stability of the building wall are improved, and a foundation is laid for normal use of the building.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of the module connection of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides an intelligent detection analysis and evaluation system for water seepage and water leakage of a wall body of a building engineering, which comprises a building dividing module, a building collecting module, a building comprehensive analysis module, a seepage grade evaluation and identification module, a seepage source matching module, a management database and a seepage early warning processing module. The connection relation between the modules is as follows: the building dividing module is connected with the building collecting module, the building comprehensive analysis module is connected with the building collecting module and the leakage grade evaluation and identification module respectively, the leakage grade evaluation and identification module is connected with the leakage source matching module and the leakage early warning processing module respectively, and the management database is connected with the building comprehensive analysis module, the leakage source matching module and the leakage early warning processing module respectively.
The building dividing module is used for dividing each building detection area according to the key parts of the building to be detected and numbering each building detection area sequentially.
Further illustratively, the key parts of the building to be inspected include, but are not limited to: the building window comprises a building main body outer wall, a building window outer wall and a building basement wall, wherein the building main body outer wall and the building basement wall are divided according to a set area.
The building acquisition module is used for acquiring leakage conditions of all building detection areas, so as to obtain leakage data of all building detection areas.
In one embodiment, the leakage data includes building wall moisture content, wall water mark length, water drop number, wall mold area, and wall color area.
It should be noted that, the collecting the leakage condition of each building detection area specifically includes: the building to be detected is divided into building detection areas according to the key parts, and the building detection areas are sequentially numbered as i=1, 2.
And setting humidity sensors in key areas of the detection areas of the buildings, identifying characters on the humidity sensors through image processing and pattern identification technologies, extracting the identified characters, and transmitting the extracted characters to a management database to obtain the water content Q i of the building wall body in the key areas of the detection areas of the buildings. The moisture content of the wall reflects the humidity and the moisture content of the wall, the influence of water seepage and water leakage on the wall is known, a reference is provided for quickly positioning a seepage source, further damage and cost are avoided, a specific numerical value is provided in the monitoring process and is compared with a set threshold value to determine whether the safety range is exceeded, and corresponding repair and protection measures are timely taken.
Setting a regular snapshot camera in a key area of each building detection area, carrying out infrared thermal imaging technology on the key area of each building detection area according to a set time node, photographing and detecting to obtain an infrared thermal imaging image for visualization, selecting an area with a bright color value in the infrared thermal imaging image to form a photo image according to the sensitivity of the infrared thermal imaging device and the background temperature of the picture, carrying out identification processing on the detected wall water mark in the image, and measuring to obtain the wall water mark length L i of the key area of each building detection area. The wall water mark length provides a quantification result, the wall moisture degree is analyzed based on the observation and measurement of images, the damage to wall structures and materials is reduced, the horizontal propagation range of water is intuitively embodied, and the water seepage and leakage problems are evaluated and compared.
And (3) carrying out gray value processing on the photo image corresponding to the key area of each building detection area, extracting gray values of pixel points obtained after gray processing of each building detection area, comparing the gray values of the pixel points with a set gray value range of water drops or mould, judging the pixel points as water drops if the gray value of the pixel points of a certain building detection area is within the set gray value range of the water drops, counting the number of building water drops in the key area of each building detection area and recording as N i, judging the pixel points as mould if the gray value of the pixel points of the certain building detection area is within the set gray value range of the mould, and counting the mould area of the building wall of the key area of each building detection area and recording as S i. The water seepage degree can be estimated by calculating the quantity of water drops on the wall, the water drop gathering area is usually the wall seepage source, the problem area can be quickly found, the water seepage source can be positioned, the water seepage distribution condition can be displayed, and guidance is provided for repair work. Meanwhile, the measurement of the mould area is helpful to find potential leakage problems or indirectly indicate humidity problems existing in the wall due to the extremely humid mould growth environment, provides the relative severity of water seepage and water leakage and information related to the area, and indicates the direction for the area further investigated by the wall.
Further, the method for obtaining the mildew area of the building wall in the key area of each building detection area comprises the following steps: and counting the number of mould pixel points of the building wall body in the key area of each building detection area, and multiplying the number of mould pixel points by the corresponding area of the set pixel points to obtain the mould area of the building wall body in the key area of each building detection area.
And (3) carrying out binarization processing on the photo image corresponding to the key area of each building detection area, setting the gray value of the pixel point on the image to be 0 or 255, enabling the whole image to show obvious visual effects of only black and white, counting the image area of the key area of each building detection area expressed as 0, obtaining the building wall color change area of the key area of each building detection area, and recording as T i. The wall color change area intuitively shows the water seepage and water leakage area, so that the problem area is easier to find and position, and further depth measurement and water seepage area estimation are realized. The method is favorable for determining the change trend of the water seepage problem, evaluating the effectiveness of the repair measures and adjusting the subsequent maintenance plan.
Further, the key area of each building detection area is a circle with the center point of the building detection area as the center, the set distance as the radius, and the area of the circle is used as the key area of each building detection area.
The method has wide coverage for collecting the leakage data of the building wall, collects the water content of the wall, the length of water marks on the wall, the number of water drops, the mildew area of the wall and the color change area of the wall, and changes the moisture data and visualizes the wall, so that the moisture area of the wall is more obvious and accurately positioned, the potential leakage problem is effectively supported, and a good foundation is laid for subsequent wall inspection and repair. Meanwhile, the concrete embodiment of the leakage data conversion leakage severity level is analyzed, the comprehensiveness of leakage problems is improved, the cost of wall repair is reduced, and the risk of wall leakage is further avoided.
The building comprehensive analysis module is used for processing and analyzing according to the leakage data of each building detection area, so as to obtain the leakage severity evaluation coefficient of each building detection area.
In one embodiment, the leakage severity assessment factor for each building detection zone is analyzed by: putting the water content Q i, the length L i of the water mark, the number N i of water drops, the mildew area S i and the color change area T i of the wall body of the building in the key area of each building detection area into a formulaAnd obtaining a leakage severity assessment coefficient theta i of each building detection area, wherein Q ', L ', N ', S ', T ' are respectively set building wall water content, wall water mark length, wall water drop number, wall mildew area and wall color change area which meet the building engineering standard, and mu 1、μ2、μ3、μ4、μ5 is respectively set influence factors corresponding to the building wall water content, the wall water mark length, the water drop number, the wall color change area and the wall mildew area.
The leakage grade evaluation and identification module is used for analyzing the leakage severity evaluation coefficients of the detection areas of the buildings, and further screening the water seepage areas and the residual areas of the buildings to be detected.
In one embodiment, the analysis of the leakage severity assessment coefficients for each building detection zone specifically comprises: comparing the leakage severity assessment coefficient of each building detection area with a set leakage severity assessment coefficient threshold, if the leakage severity assessment coefficient of a certain building detection area is larger than the set leakage severity assessment coefficient threshold, taking the building detection area as a water seepage area, otherwise, taking the building detection area as a residual area, and further screening each water seepage area and each residual area of the building to be detected.
According to the invention, the building to be detected is divided into the building detection areas according to the key parts, and the data acquisition and comprehensive analysis are carried out on the building detection areas, so that the deep excavation of the wall body leakage key areas is realized, the water seepage and water leakage detection on the wall body key parts is effectively carried out in time, and more accurate data and analysis results are provided for the subsequent leakage grade evaluation.
The leakage source matching module is used for analyzing leakage sources of all the water seepage areas according to leakage associated data corresponding to all the historical water seepage of the building to be detected in a set historical period and displaying the leakage sources of all the water seepage areas.
In one embodiment, the analyzing the leakage source of each water permeable region specifically comprises: extracting leakage related data corresponding to each historical water seepage of a building to be detected in a set historical period in a management database, wherein the leakage related data comprise leakage severity assessment coefficients, leakage areas and water seepage sources, and the leakage severity assessment coefficients of the water seepage areas screened out according to the leakage severity assessment coefficients of the detection areas of the building are marked as theta t, t is the number t=1, 2 and..s of the water seepage areas.
And (3) obtaining leakage areas of the water seepage areas, comparing the leakage areas with leakage areas of each historical water seepage of the building to be detected in a set historical period, and recording the leakage overlapping area of each water seepage area and the leakage area corresponding to each historical water seepage in the set historical period as S ty, wherein y is the number of the leakage area corresponding to each historical water seepage, and y=1, 2.
Substituting the leakage severity evaluation coefficient of each water seepage area and the leakage overlapping area of each water seepage area corresponding to each historical water seepage area in the set historical period into the information coincidence degree formulaObtaining the information coincidence degree xi ty,θ'y and delta S 'of each seepage area and each seepage area corresponding to each historical seepage in a set historical period, wherein the information coincidence degree xi ty,θ'y and delta S' are leakage severity assessment coefficients corresponding to the y-th historical seepage in the set historical period of the building to be detected, the allowable leakage overlapping area difference value of the building under the safety condition, alpha 1、α2 is a set seepage area leakage severity assessment coefficient and a weight influence factor corresponding to the area of the seepage area, P 1、P2 is a set constant, and e is a natural constant. By computational measurement of the leakage severity assessment factor and the area of the leakage area, the severity of the leakage problem can be assessed to assess the extent and complexity of the repair work and thereby determine the preferentially treated water penetration area.
According to the invention, the leakage sources of the seepage areas of the building are matched, the history carry-over problem of the seepage areas is accurately prevented, the task amount of source detection of the seepage areas is reduced, the detection efficiency of the seepage areas of the wall body of the building is increased, the sources and the positions of the seepage areas are effectively mastered, and the reliability guarantee is provided for the subsequent wall body maintenance work.
The management database is used for storing leakage associated data corresponding to each time of historical water seepage of the building to be detected in a set historical period and standard leakage data meeting the building engineering standard, and storing leakage severity evaluation coefficients corresponding to each time of detection of each building detection area of the building to be detected in a set historical period and leakage overlapping area difference allowed by the building under the safety condition.
It should be explained that the analyzing the leakage source of each water seepage area specifically further includes: and taking each historical water seepage of which the corresponding information coincidence degree of each water seepage area is larger than a set information coincidence degree threshold value as each reference water seepage, further extracting water seepage sources corresponding to each historical water seepage of a building to be detected in a management database in a set historical period, obtaining water seepage sources corresponding to each reference water seepage of each water seepage area, further obtaining the quantity of each water seepage source corresponding to each water seepage area, screening the water seepage sources with the largest quantity corresponding to each water seepage area, and taking the water seepage sources as the water seepage sources of each water seepage area. The water seepage source can be accurately positioned by determining the water seepage source, the water seepage area is focused on a specific problem area, detection and water seepage source are timely carried out, detection and repair time and cost are saved, the wall body is repaired in a targeted manner, the repair process is more efficient, and subsequent damage and problem occurrence are prevented.
The leakage early warning processing module is used for detecting leakage conditions of all the residual areas of the building to be detected, further evaluating leakage probability coefficients of all the residual areas, and carrying out corresponding early warning processing.
In one embodiment, the detecting the leakage condition of each remaining area of the building specifically includes: extracting a leakage severity assessment coefficient theta mv corresponding to each detection of each residual area of a building to be detected in a set historical time period in a management database, wherein m is the number of each residual area, m=1, 2,..l, v is the number of each detection corresponding to the residual area, v=1, 2,..u, and substituting the leakage severity assessment coefficient growth rate analysis formulaObtaining the leakage severity assessment coefficient growth rate RX m of each residual area, wherein theta m(v+1) is the leakage severity assessment coefficient of the mth residual area corresponding to the (v+1) th detection in the set historical time period of the building to be detected.
Extracting the environment temperature of each residual area of the building to be detected in the set historical time period from the weather bureau, recording as omega mv, and substituting the environment temperature evaluation coefficient change rate analysis formulaObtaining the change rate RH m of the environmental temperature evaluation coefficient of each residual area, wherein omega m(v+1) is the environmental temperature of the mth residual area corresponding to the (v+1) th detection in the set historical time period of the building to be detected. By measuring the leakage severity evaluation coefficient growth rate and the environmental temperature evaluation coefficient change rate, the water seepage and leakage capacity of the wall body in each residual area can be better evaluated and monitored, whether the water seepage problem is aggravated or not is determined, and whether the wall body is further damaged or not is further determined, so that proper maintenance and preventive measures are taken, and potential damage and maintenance cost are reduced.
It should be explained that the evaluation of the leakage probability coefficient of each remaining area specifically includes: substituting the leakage severity evaluation coefficient growth rate and the environment temperature evaluation coefficient change rate of each residual area into the leakage probability coefficient lambda m＝ρ1*RXm+ρ2*RHm of each residual area to obtain the leakage probability coefficient lambda m of each residual area, wherein rho 1、ρ2 is the set leakage severity evaluation coefficient and the influence weight factor corresponding to the environment temperature, rho 1+ρ2 =1, comparing the obtained leakage probability coefficient of each residual area with the set leakage probability coefficient threshold, and repairing and maintaining the residual area if the leakage probability coefficient of a certain residual area is larger than or equal to the set leakage probability coefficient threshold. The water seepage and water leakage problems are quantified through the measurement and calculation of the seepage probability coefficient, and the water seepage position and degree are determined, so that objectivity and accuracy are realized in water seepage risk assessment, wall risk areas are predicted in advance when seepage does not occur, corresponding preventive measures are taken in time, and a repair plan is formulated more accurately. In addition, after the water seepage and water leakage repairing measures are taken, the seepage possibility coefficient is measured again, the repairing effect can be evaluated, and if the seepage possibility coefficient is obviously reduced, the effectiveness of the repairing measures can be confirmed, so that the subsequent water leakage problem is avoided.
According to the invention, the system analysis is carried out on each residual area of the building wall, and effective data and theoretical support are provided for leakage assessment and prediction of each residual area according to the analysis of historical factors and environmental changes, so that the safety and stability of the building wall are improved, and a foundation is laid for normal use of the building.
The foregoing is merely illustrative and explanatory of the principles of this invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this invention or beyond the scope of this invention as defined in the claims.
Claims (7)
1. The intelligent detection, analysis and evaluation system for the water seepage and water leakage of the wall body of the building engineering is characterized by comprising a building dividing module, a building collecting module, a building comprehensive analysis module, a leakage grade evaluation and identification module, a leakage source matching module, a management database and a leakage early warning processing module;
The building dividing module is used for dividing each building detection area according to the key parts of the building to be detected and numbering each building detection area sequentially;
The building acquisition module is used for acquiring leakage conditions of each building detection area so as to obtain leakage data of each building detection area;
the building comprehensive analysis module is used for processing and analyzing according to the leakage data of each building detection area so as to obtain the leakage severity evaluation coefficient of each building detection area;
The leakage grade evaluation and identification module is used for analyzing the leakage severity evaluation coefficients of the detection areas of the buildings so as to screen the water seepage areas and the residual areas of the buildings to be detected;
The leakage source matching module is used for analyzing leakage sources of all the water seepage areas according to leakage associated data corresponding to all the historical water seepage of the building to be detected in a set historical period and displaying the leakage sources of all the water seepage areas;
The management database is used for storing leakage associated data corresponding to each time of historical water seepage of the building to be detected in a set historical period and standard leakage data meeting the building engineering standard, and storing leakage severity evaluation coefficients corresponding to each time of detection of each building detection area of the building to be detected in a set historical period and leakage overlapping area difference values allowed by the building under the safety condition;
The leakage early warning processing module is used for detecting leakage conditions of all the residual areas of the building to be detected, further evaluating leakage probability coefficients of all the residual areas and carrying out corresponding early warning processing;
The leakage condition collection is carried out on each building detection area, and the method specifically comprises the following steps:
dividing each building detection area of the building to be detected according to the key parts, and numbering each building detection area sequentially to be ；
Setting humidity sensors in key areas of each building detection area, recognizing characters on the humidity sensors through image processing and pattern recognition technology, extracting the recognized characters and transmitting the extracted characters to a management database to obtain the water content of building walls in the key areas of each building detection area；
Setting regular snap cameras in key areas of detection areas of all buildings, carrying out infrared thermal imaging technology on the key areas of the detection areas of all buildings according to set time nodes, photographing and detecting to obtain infrared thermal imaging images for visualization, selecting areas with bright color values in the infrared thermal imaging images to form photo images according to the sensitivity of infrared thermal imaging equipment and the background temperature of the pictures, carrying out identification processing on detected wall water marks in the images, and measuring the length of the wall water marks of the key areas of the detection areas of all buildings；
Performing gray value processing on the photo image corresponding to the key area of each building detection area, extracting gray value of each pixel point obtained after gray processing of each building detection area, comparing the gray value of each pixel point with a set gray value range of water drops or mould, judging a pixel point as a water drop if the gray value of the pixel point of a certain building detection area is within the set gray value range of the water drops, counting the number of building water drops in the key area of each building detection area and recording as the water dropsIf the gray value of a pixel point in a detection area of a certain building is within a set mold gray value range, the pixel point is judged to be mold, and the mold area of a building wall in a key area of each detection area of the building is counted and recorded as/>；
Binarizing photo images corresponding to key areas of all building detection areas, setting gray values of pixel points on the images to be 0 or 255, enabling the whole images to show obvious visual effects of only black and white, counting image areas of the key areas of all building detection areas expressed as 0, obtaining building wall color change areas of the key areas of all building detection areas, and recording the building wall color change areas as；
The water content of the building wall body in the key area of each building detection area is determinedWall water mark length/>Quantity of wall water dropsWall mildew area/>Wall colour change area/>To-be-put formulaObtaining the leakage severity assessment coefficient/>, of each building detection areaIn/>Respectively, the water content of the building wall body, the length of the water mark of the wall body, the number of water drops of the wall body, the mildew area of the wall body and the color change area of the wall body which are set to meet the building engineering standard,/>The water content, the length of the water mark, the number of water drops, the color change area and the mildew area of the wall body of the building are respectively set.
2. The intelligent detection, analysis and evaluation system for water seepage and water leakage of a building engineering wall body according to claim 1, wherein the intelligent detection, analysis and evaluation system is characterized in that: the leakage data comprise the water content of the building wall, the length of the water mark of the wall, the number of water drops, the mildew area of the wall and the color change area of the wall.
3. The intelligent detection, analysis and evaluation system for water seepage and water leakage of a building engineering wall body according to claim 1, wherein the intelligent detection, analysis and evaluation system is characterized in that: the analysis of the leakage severity assessment coefficient of each building detection area specifically comprises the following steps:
Comparing the leakage severity assessment coefficient of each building detection area with a set leakage severity assessment coefficient threshold, if the leakage severity assessment coefficient of a certain building detection area is larger than the set leakage severity assessment coefficient threshold, taking the building detection area as a water seepage area, otherwise, taking the building detection area as a residual area, and further screening each water seepage area and each residual area of the building to be detected.
4. The intelligent detection, analysis and evaluation system for water seepage and water leakage of a building engineering wall body according to claim 1, wherein the intelligent detection, analysis and evaluation system is characterized in that: the analysis of leakage sources in each water seepage area specifically comprises the following steps:
Extracting leakage correlation data corresponding to each historical water seepage of a building to be detected in a set historical period in a management database, wherein the leakage correlation data comprises leakage severity assessment coefficients, leakage areas and water seepage sources, and marking the leakage severity assessment coefficients of the water seepage areas screened out according to the leakage severity assessment coefficients of the detection areas of the building as ，/>Numbering of the various Water permeable regions/>；
Obtaining leakage areas of the water seepage areas, comparing the leakage areas with leakage areas of each historical water seepage of a building to be detected in a set historical period, and obtaining leakage overlapping areas of the leakage areas corresponding to each historical water seepage in the set historical period as，/>For each history of seepage, the number of the seepage area is corresponding to the number of the seepage area,/>;
Substituting the leakage severity evaluation coefficient of each water seepage area and the leakage overlapping area of each water seepage area corresponding to each historical water seepage area in the set historical period into the information coincidence degree formulaObtaining the information coincidence degree/>, of each water seepage region and the leakage region corresponding to each historical water seepage in the set historical period，/>Setting a leakage severity evaluation coefficient corresponding to the y-th historical seepage in a historical period for a building to be detected, and a leakage overlapping area difference value allowed by the building under the safety condition,/>Evaluating coefficient for the seepage severity of the set seepage area, and weight influence factor corresponding to the area of the seepage area,/>To set a constant,/>Is a natural constant.
5. The intelligent detection, analysis and evaluation system for water seepage and water leakage of a building engineering wall body according to claim 4, wherein the intelligent detection, analysis and evaluation system is characterized in that: the analyzing the leakage sources of each water seepage area specifically further comprises:
And taking each historical water seepage of which the corresponding information coincidence degree of each water seepage area is larger than a set information coincidence degree threshold value as each reference water seepage, further extracting water seepage sources corresponding to each historical water seepage of a building to be detected in a management database in a set historical period, obtaining water seepage sources corresponding to each reference water seepage of each water seepage area, further obtaining the quantity of each water seepage source corresponding to each water seepage area, screening the water seepage sources with the largest quantity corresponding to each water seepage area, and taking the water seepage sources as the water seepage sources of each water seepage area.
6. The intelligent detection, analysis and evaluation system for water seepage and water leakage of a building engineering wall body according to claim 1, wherein the intelligent detection, analysis and evaluation system is characterized in that: the detecting the leakage condition of each residual area of the building to be detected specifically comprises the following steps:
extracting leakage severity evaluation coefficients of each residual area of a building to be detected in a set historical time period corresponding to each detection in a management database ，/>For each remaining region number,/>，/>For the number of each detection corresponding to the residual area,/>Substituting the leakage severity evaluation coefficient growth rate analysis formulaObtaining the leakage severity evaluation coefficient growth rate/>, of each residual regionIn/>Evaluating a coefficient for the leakage severity degree of the (v+1) th detection of the mth residual area of the building to be detected in a set historical time period;
Extracting the environment temperature of each residual area of the building to be detected in the set historical time period from the weather bureau, and recording as Substituting the environmental temperature evaluation coefficient change rate analysis formulaObtaining the change rate/>, of the environmental temperature evaluation coefficient of each residual regionIn/>And the mth remaining area of the building to be detected corresponds to the v+1th detected ambient temperature in the set historical time period.
7. The intelligent detection, analysis and evaluation system for water seepage and water leakage of a building engineering wall body according to claim 1, wherein the intelligent detection, analysis and evaluation system is characterized in that: the evaluation of the leakage probability coefficient of each remaining area specifically includes:
Substituting the leakage severity evaluation coefficient increase rate and the ambient temperature evaluation coefficient change rate of each residual region into the leakage probability coefficient of each residual region Obtaining leakage probability coefficient/>, of each residual regionIn/>Evaluating the coefficient for the set seepage severity, the influence weight factor corresponding to the ambient temperature, and/>Comparing the obtained leakage probability coefficient of each residual area with a set leakage probability coefficient threshold, and repairing and maintaining the residual area if the leakage probability coefficient of a certain residual area is larger than or equal to the set leakage probability coefficient threshold.
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| CN111814858A (en) * | 2020-06-30 | 2020-10-23 | 华北水利水电大学 | Novel underground diaphragm wall joint waterproof structure, water leakage detection system and method |
| CN112016848A (en) * | 2020-09-11 | 2020-12-01 | 范玲珍 | Intelligent detection management system for quality supervision, acceptance and acceptance of constructional engineering based on data scheduling |
| CN114359416A (en) * | 2022-03-17 | 2022-04-15 | 山东水利建设集团有限公司 | Building outer wall hollowing leakage abnormity detection and positioning method |
| CN114511014A (en) * | 2022-01-21 | 2022-05-17 | 北京城建勘测设计研究院有限责任公司 | Subway tunnel water leakage detection system and method based on image deep learning algorithm |
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| CN112016848A (en) * | 2020-09-11 | 2020-12-01 | 范玲珍 | Intelligent detection management system for quality supervision, acceptance and acceptance of constructional engineering based on data scheduling |
| CN114511014A (en) * | 2022-01-21 | 2022-05-17 | 北京城建勘测设计研究院有限责任公司 | Subway tunnel water leakage detection system and method based on image deep learning algorithm |
| CN114359416A (en) * | 2022-03-17 | 2022-04-15 | 山东水利建设集团有限公司 | Building outer wall hollowing leakage abnormity detection and positioning method |
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